most citedRiemannian Denoising Diffusion Probabilistic Models

9 citations

7 papers

cs.LG20269 cited

Riemannian Denoising Diffusion Probabilistic Models

Zichen Liu, Wei Zhang, Christof Schütte +1

We propose Riemannian Denoising Diffusion Probabilistic Models (RDDPMs) for learning distributions on submanifolds of Euclidean space that are level sets of functions, including mo…

cs.MA20261 cited

A Hybrid ABM-PDE Framework for Real-World Infectious Disease Simulations

Kristina Kehrer, Tim O. F. Conrad

This paper presents a hybrid modeling approach that couples an Agent-Based Model (ABM) with a partial differential equation (PDE) model in an epidemic setting to simulate the spati…

math.OC2026

Optimisation models for the design of multiple self-consumption loops in semi-rural areas

Yohann Chasseray, Mathieu Besançon, Xavier Lorca +1

Collective electricity self-consumption gains increasing interest in a context where localised consumption of energy is a lever of sustainable development. While easing energy dist…

quant-ph2026

Most incompatible measurements and sum-of-squares optimisation

Sébastien Designolle

Measurement incompatibility, or joint measurability, is a cornerstone of quantum theory and a useful resource. For finite-dimensional systems, quantifying this resource and establi…

cs.LG20263 cited

Manifold GCN: Diffusion-based Convolutional Neural Network for Manifold-valued Graphs

Martin Hanik, Gabriele Steidl, Christoph von Tycowicz

We propose two graph neural network layers for graphs with features in a Riemannian manifold. First, based on a manifold-valued graph diffusion equation, we construct a diffusion l…

quant-ph2026

Measurement incompatibility and quantum steering via linear programming

Lucas E. A. Porto, Sébastien Designolle, Sebastian Pokutta +1

The problem of deciding whether a set of quantum measurements is jointly measurable is known to be equivalent to determining whether a quantum assemblage is unsteerable. This probl…